remove beta

website/docs/docs/dbt-versions/release-notes.md

@@ -18,6 +18,9 @@ Release notes are grouped by month for both multi-tenant and virtual private clo
 
 \* The official release date for this new format of release notes is May 15th, 2024. Historical release notes for prior dates may not reflect all available features released earlier this year or their tenancy availability.
 
+## December 2024
+- **New**: [Model notifications](/docs/deploy/model-notifications) is now generally available in dbt Cloud. These notifications alert model owners through email about any issues encountered by models and tests as soon as they occur while running a job.
+
 ## November 2024
 - **Fix**: Job environment variable overrides in credentials are now respected for Exports. Previously, they were ignored.
 - **Behavior change**: If you use a custom microbatch macro, set a [`require_batched_execution_for_custom_microbatch_strategy` behavior flag](/reference/global-configs/behavior-changes#custom-microbatch-strategy) in your `dbt_project.yml` to enable batched execution. If you don't have a custom microbatch macro, you don't need to set this flag as dbt will handle microbatching automatically for any model using the [microbatch strategy](/docs/build/incremental-microbatch#how-microbatch-compares-to-other-incremental-strategies).

website/docs/docs/deploy/model-notifications.md

@@ -3,21 +3,13 @@ title: "Model notifications"
 description: "While a job is running, receive email notifications in real time about any issues with your models and tests. "
 ---
 
-# Model notifications <Lifecycle status="beta" />
-
 Set up dbt to notify the appropriate model owners through email about issues as soon as they occur, while the job is still running. Model owners can specify which statuses to receive notifications about: 
 
 - `Success` and `Fails` for models
 - `Warning`, `Success`, and `Fails` for tests
 
 With model-level notifications, model owners can be the first ones to know about issues before anyone else (like the stakeholders). 
 
-:::info Beta feature
-
-This feature is currently available in [beta](/docs/dbt-versions/product-lifecycles#dbt-cloud) to a limited group of users and is gradually being rolled out. If you're in the beta, please contact the Support team at [email protected] for assistance or questions.
-
-:::
-
 To be timely and keep the number of notifications to a reasonable amount when multiple models or tests trigger them, dbt observes the following guidelines when notifying the owners:  
 
 - Send a notification to each unique owner/email during a job run about any models (with status of failure/success) or tests (with status of warning/failure/success). Each owner receives only one notification, the initial one.

website/docs/docs/deploy/monitor-jobs.md

@@ -13,7 +13,7 @@ This portion of our documentation will go over dbt Cloud's various capabilities
 - [Run visibility](/docs/deploy/run-visibility) — View your run history to help identify where improvements can be made to scheduled jobs.
 - [Retry jobs](/docs/deploy/retry-jobs) — Rerun your errored jobs from start or the failure point.
 - [Job notifications](/docs/deploy/job-notifications) — Receive email or Slack notifications when a job run succeeds, encounters warnings, fails, or is canceled. 
-- [Model notifications](/docs/deploy/model-notifications)<Lifecycle status="beta"/> &mdash; Receive email notifications about any issues encountered by your models and tests as soon as they occur while running a job. 
+- [Model notifications](/docs/deploy/model-notifications) &mdash; Receive email notifications about any issues encountered by your models and tests as soon as they occur while running a job. 
 - [Webhooks](/docs/deploy/webhooks) &mdash; Use webhooks to send events about your dbt jobs' statuses to other systems.
 - [Leverage artifacts](/docs/deploy/artifacts) &mdash; dbt Cloud generates and saves artifacts for your project, which it uses to power features like creating docs for your project and reporting freshness of your sources.
 - [Source freshness](/docs/deploy/source-freshness) &mdash; Monitor data governance by enabling snapshots to capture the freshness of your data sources.